Constructability assessment of social housing projects in Brazil: a quantitative benchmarking system

Avaliando a construtibilidade de projetos de habitação de interesse social no Brasil: um sistema de benchmarking quantitativo

Abstract

Social Housing Projects (HIS) are essential for reducing Brazil’s housing deficit. However, constructability problems can undermine the efficiency achieved in those projects, negatively affecting the purpose of having low cost. One approach for reducing constructability issues is to use constructability assessment systems (CAS). Existing CAS lack data that allow for setting goals, or even studies that provide an overview of constructability in HIS in each country. Thus, the aim of this research study is to develop a quantitative benchmarking constructability system for HIS. This study was based on data from 394 projects. The development of this quantitative benchmarking system pointed out the potential for improving constructability in those projects, by adopting different measures, such as the use of BIM, changing construction processes, and achieving appropriate levels of standardization. This system can support companies that wish to improve the constructability in HIS projects, by reducing the bias of evaluating only their own data, or Brazilian HIS financial institutions. This benchmarking system represents a theoretical-practical advance in the knowledge about constructability, given that similar studies are limited or non-existent in other countries.

Keywords
Constructability; Quantitative assessment; Performance management and measurement; Benchmarking; Social housing

Resumo

Projetos de Habitação de Interesse Social (HIS) são fundamentais para reduzir o déficit habitacional brasileiro. Problemas de construtibilidade podem torná-los menos eficientes, prejudicando o propósito do baixo custo. Para reduzir esses problemas, uma abordagem consiste no uso de Sistemas de Indicadores (CAS). Nos CAS existentes faltam dados que permitam definir metas, ou estudos que apontem qual o panorama num dado país. Assim, essa pesquisa objetivou desenvolver um sistema de benchmarking quantitativo de construtibilidade em HIS. Com base no método de DSR, o artefato gerado foi um sistema de benchmarking quantitativo, baseado em 394 projetos. Por meio do sistema de benchmarking quantitativo, potenciais nacionais de melhoria da construtibilidade foram evidenciados, e.g., a adoção de outros processos construtivos ou níveis razoáveis de padronização. O sistema pode oferecer suporte a empresas que desejem melhorar a construtibilidade de seus projetos HIS (reduzindo o viés de avaliação de seus próprios dados), ou a agências de financiamento de HIS. Esse sistema de benchmarking representa um avanço teórico-prático no conhecimento sobre construtibilidade, haja vista que estudos similares são limitados ou inexistem em outros países.

Palavras-chave
Construtibilidade; Avaliação quantitativa; Gestão e medição do desempenho; Benchmarking; Habitação de Interesse Social

Introduction

Social Housing Projects (HIS) plays a central role in ensuring the population's right to suitable housing. This is highly relevant in Brazil, which has an estimated housing deficit of six million homes that need to be built efficiently and economically, while also meeting quality requirements (Eloy et al., 2021; Gurmu; Mahmood, 2024). Improving performance in HIS projects is essential to ensure the delivery of affordable housing units, considering for buyer’s purchasing power (Moura Jorge et al., 2017). Increasing in the efficiency of housing projects can potentially allow for the construction of many homes, optimizing the use of public resources (Moraes, 2018). Several aspects of design and execution need to be improved in order to make HIS projects more efficient. One of the key strategies is to improve constructability at the design phase of construction projects. Constructability is an inherent characteristic of construction projects reflecting:

1. how easy they are to build;

2. the optimal use of available resources; and

3. the integration of construction operations into the design phase (ASCE, 1991; Sabbatini, 1989).

Solutions for constructability issues have already been proposed in the literature, including:

1. constructability analyses involving designers and builders (Falotico, 2017);

2. constructability reviews by using BIM tools (Luo; Zadeh; Staub-French, 2024);

3. the provision of complete specifications, including production drawings (Barbosa, 2013); and

4. Constructability Assessment Systems (CAS), i.e., performance measurement systems which can be used to undertake a quantitative assessment of constructability (Abreu; Marchiori; Oviedo Haito, 2019).

Constructability, sustainability and quality are intangible characteristics (Sing et al., 2022). Assessing them requires the development of theoretical terms, constructs, and concepts before establishing CAS indicators (Selitto; Ribeiro, 2004). Constructability Principles, such as those proposed by Kifokeris and Xenidis (2017), can serve as a starting point for the definition of a set of metrics. Moreover, aspects such as simplification, reduction in the number of parts or components, standardization and modularity can also play a key role in this type of performance evaluation (Wang et al., 2025).

There are currently several CAS in use in different countries, such as Singapore and Hong Kong. Singapore's CAS, known as the Buildable Design Appraisal System (BDAS), assesses the constructability of projects during the design stage (Lam et al., 2006; Osuizugbo et al., 2022). A similar system was proposed for Hong Kong, called Buildability Assessment Model (BAM) (Wong; 2007). However, BAM is more detailed than BDAS as it includes finishing systems. None of those systems, however, incorporates aspects related to construction site organization.

In other countries, similar research has led to the development of additional CAS. Jarkas (2010) and Kannan and Santhi (2018) proposed a CAS focused exclusively on formwork systems execution. Tauriainen, Puttonen and Saari (2015) introduced the Instantiation Constructability Assessment Methodology (ECAM) for evaluating only structural systems in Finnish construction projects. It is evident that such indicators do not encompass all the constructability principles proposed by Kifokeris and Xenidis (2017), as they lack considerations related to site management and site layout design.

Due to the differences in construction technologies that exist between countries, it is not adequate to simply adopt one of those systems in Brazil. These must be adapted to the context, if possible, taking into account the best features of each system, to create a comprehensive and viable CAS for Brazil.

In this regard, in Brazil, Narloch (2015) and Carvalho (2021) proposed a CAS for medium to high-end residential projects. Abreu (2020) proposed the Housing Constructability Assessment System (HCAS), specifically targeting Brazilian HIS projects.

Performance measurement systems (such as a CAS) can support decision-making in construction, enabling the establishment of objective criteria such as success factors, goal setting, motivation, and benchmarking (Cândido, 2015). For each indicator, a goal is required, i.e., a score that represents the minimum value to be achieved, since it is neither possible nor feasible obtain maximum scores for all indicators (Melnik et al., 2014; Franco-Santos et al., 2007).

Existing CAS do not provide goals for their indicators. Moreover, they have been applied only sparingly in real projects, which limits their practical usefulness (Abreu; Marchiori, 2023). In the absence of goals for evaluating HIS projects, comparisons are typically made only with other projects from the same company, which can introduce evaluation bias. The ideal scenario would involve setting goals based on indicators derived from a broader sample of companies from the construction sector.

Another practical limitation is the use of CAS to understand the potential for improving constructability in each region or country, such as Brazil. In fact, previous qualitative research studies (e.g. Safapour; Kermanshachi; Ramaji, 2023; Trigunarsyah, 2004) have not suggested which indicators are most critical in a CAS. Defining such indicators would represent a practical advance and provide valuable knowledge for addressing constructability problems.

These limitations could be mitigated by developing a quantitative constructability benchmarking system. Such a system could support both the improvement of individual projects and a broader understanding of national constructability challenges.

Given this context, the aim of this research was to develop a quantitative benchmarking system for constructability in HIS. Promoting constructability improvements in these projects not only enhances construction quality and productivity in the construction industry, but also supports the effort to reduce housing deficits in countries like Brazil (Moura Jorge et al., 2017; Moraes, 2018).

Literature review

Constructability Assessment through HCAS

HCAS was developed by Abreu (2020) to assess the HIS constructability. This assessment system is based on Singapore’s CAS (BDAS) and additionally incorporates productivity metrics from a key Brazilian costing system, named National System of Costs and Construction Indices (SINAPI).

This CAS consists of 23 indicators, aggregated into partial indicators by construction stage and an overall indicator (IC), which represents the entire project. IC is calculated by using a weighted average: the adopted weights were obtained through a questionnaire answered by a sample of more than one hundred engineering and architecture professionals, and subsequently validated and adjusted by five experienced HIS experts. Those weights are presented in the "Quantitative Benchmarking Calculation" section.

Constructability benchmarking

Camp (1992, p. 3) defines benchmarking as “[…] the continuous process of evaluating products, services, and practices of competitors [...] identifying how success was achieved and adapting strategies”. According to Kyrö (2003), benchmarking can be applied in organizations of various sizes, both in the public and private sectors, enabling improvements in activities, processes, or management. Different types of benchmarking are presented in Table 1.

Table 1
Benchmarking types

According to…	Types	Comparison
Objects	Product	Among a company’s own products and those of its competitors.
	Process	Among business processes, either within the same sector or across different sectors.
	Strategies	Strategies aimed at obtaining new competitive advantages.
Parameter	Performance	Objective, based on indicator systems.
	Best practices	Lists of best practices and their frequency of occurrence.
Application	Sectorial	Across different sectors (e.g., supermarkets and automotive industry).
	Internal (company)	Data from the same company, both current and historical.
	External (company)	Among different companies, with a competitive focus.
	Structural	Among companies in different locations, economic contexts or production chains.

Formoso et al. (2003) and Albertin, Kohl and Elias (2015) point out that the benchmarking process may involve more than one of the evaluations presented in Table 1. They further note that processes yielding richer results and involving more complex implementation include external business benchmarking and performance benchmarking.

Some researchers have conducted benchmarking related to constructability, such as the studies carried out by Al-Yousif (2001) and Trigunarsyah (2004). The latter identified practices in Indonesia such as early contractor involvement during the feasibility and design stages, suggesting materials, structural systems, and other collaborations. Safapour, Kermanshachi and Ramaji (2023) also observed good practices, such as the adoption of constructability principles, and conducting analyses on the cost and schedule impacts of these practices in construction projects. However, those studies have not developed performance evaluation and external benchmarking, or have done so only in a very limited way. External benchmarking is mentioned by Lam et al. (2008), who presented overall constructability assessments using BAM across nine public and private projects. Lam and Wong (2009) conducted external benchmarking of project assessments using BAM and examined the relationship between constructability and variables such as cost, time, quality, and safety, finding positive correlations between performance and constructability indicators.

Abreu and Marchiori (2023) highlighted that only a few studies have explored the use of performance metrics nor external benchmarking data. However, the same authors pointed out that the number of projects evaluated in those studies is small, which restricts goal setting and improvement initiatives. This limitation also constraints the input data available for internal business benchmarking. Given the CAS generate evaluations based on indicators, a careful calculation of each indicator within the system is required. Another gap identified by those authors is the limited availability of benchmarking data for the construction sector as a whole, as previous studies consider at most three projects of the same typology (i.e., with the same construction process, occupancy type, or scale). The small number of projects limits the statistical significance in the analyses produced. Lam and Wong (2009) analyzed seventy-seven projects but did not provided details on the influencing constructability factors or project characteristics, presenting only overall project benchmarking data. In addition, their evaluation employed constructs or concepts from BAM assessed through Likert scales, meaning that the resulting average indicators do not correspond to a benchmark of overall constructability indicators.

The insufficiency of benchmarking data (and of performance goals, consequently) indicates that existing CAS have not yet reached a sufficient level of maturity to be used as mechanisms to support improvement. Although all the indicators of a CAS are relevant to constructability, a company will not excel in every performance dimension, making it necessary to focus on those that represent the most critical competitive criteria, as defined by benchmarking studies (Melo, 2001). Jones and Kaluarachchi (2008) emphasized the importance of conducting benchmarking to foster innovations, including in HIS.

Research method

Design Science Research (DSR) was the methodological approach adopted in this investigation, which can be defined as a rigorous process of designing new artifacts or improving existing ones to solve problems (Lacerda et al., 2013). In DSR, artifacts are meant to solve classes of similar problems, differently from solving specific problems in the

context of single organizations (Santana; Pereira; Mattos, 2023). An artifact can be either developed or improved during DSR in order to effectively solve a problem (Conejero et al., 2017).

In this research, constructability performance measurement is addressed, which can be regarded as a class of practical problems, and proposed artifacts (i.e. CAS and benchmarking systems) have been evaluated (Lacerda et al., 2013). In this study, the proposed artifact is a quantitative benchmarking system for constructability in HIS.

Figure 1 schematically presents the steps of the research method. Each of these steps will be described in the following items.

Figure 1
Method

Awareness

The main outcome of this step was understanding this problem, which an essential task for devising the proposed solution. An integrative literature review was carried out to identify the knowledge gap (Botelho; Cunha; Macedo, 2011) - this is presented in the introduction and literature review section of this paper. The main topics involved were constructability, performance measurement systems, performance indicators, and benchmarking. An integrative literature review is a study with a rigorous selection of primary and secondary references (whereas systematic reviews cover only primary references). It has well-defined and documented search criteria (keywords, databases, and topic) and inclusion and exclusion mechanisms (Botelho; Cunha; Macedo, 2011). Several existing CAS were identified in the literature. However, they had limited impact in continuous improvement due to their limited scope and the lack of indicators. For that reason, the proposed artifact developed in this investigation is a quantitative benchmarking system for constructability in HIS.

Suggestion

In the suggestion phase, the first version of the artifact was devised, considering some existing CAS described in the literature. As discussed in the introduction section, most of them were limited to the evaluation of specific construction elements (e.g. structural components) or typologies (e.g. residential projects for medium or higher medium class), which are very different from HIS projects in Brazil. The only one devised for HIS was HCAS, proposed by Abreu (2020), and it was developed to evaluate all project phases. However, it has the limitation of the absence of goals in terms of metrics. For that reason, the decision was made to use this CAS as the point of departure for devising the benchmarking system.

Development

In the development phase, a quantitative external business benchmarking study (see Table 1) was conducted, which has a broader scope, eliminating the bias inherent in data collected from a single company. Another important aspect was to prioritization of data from real projects, excluding techniques such as simulations by using archetypes, as commonly used in energy benchmarking (Geraldi, 2021). At the end of this phase, goals were established for each indicator which is essential for a quantitative benchmarking system.

Sampling and data collection

The first stage of the development step was to create a database of HIS projects which was large enough to allow for statistical analysis of the results. The projects analyzed in this study are part of the HIS program Minha Casa, Minha Vida, which is financed by the National Saving Bank (Caixa Econômica Federal). A list of ongoing HIS projects from 2018 was obtained from the website of that bank. That list has the names of the construction companies in charge of building multi-family social housing projects (Brasil, 2018).

For the sample selection, single-family residential condominiums or rural settlement programs were excluded, leaving only multi-family projects. Among these, 668 companies were identified, which delivered 3,199 projects. These companies were organized alphabetically and by the number of projects executed, with the objective of prioritizing the search for current project data from companies with the most significant to the lowest level of activity.

This research was conducted through two main approaches: (1) administering a structured questionnaire – a strategy also adopted by Hwang, Fan Tan and Shatish (2013) in their benchmarking research – and (2) directly collecting project information from construction company websites, including plans, photographs, project data, location, and additional details such as construction methods and specifications. Relying solely on questionnaires or solely on data from companies (without combining the results) would not have been sufficient to obtain all the required benchmarks. These two sources of evidence were necessary to feed the constructability benchmark system.

The analyzed constructability dataset comprised 394 projects. These projects were delivered by 83 companies, corresponding to 31 % of the HIS market share in Brazil (Caixa Econômica Federal, 2018). The companies varied in size, ranging from 1 to 70 HIS projects in 2018, with an average of 11.96 and a median of 8 projects per company.

Questionnaire on constructability

A questionnaire to assess constructability was sent to professionals from 70 % of these companies (the remaining 30 % could not be located through social networks or company websites). Using Cochran's formula and considering the parameters applied by Aboseif and Hanna (2023), the minimum required sample size was calculated as 29 responses. In total, 73 responses were collected, ensuring the necessary representativeness for the sampling.

The structured questionnaire, designed using Google Forms, was configured to collect part of the 23 HCAS indicators. In addition, respondents were asked to submit their HIS designs to further compose the sample: however, only 8 % provided them. The questionnaire¹ began with the Informed Consent Form (ICF) previously approved by the research institution’s Ethics Committee, followed by questions for sample characterization (e.g., profession). The subsequent questions focused on the indicators (for example, "Which construction process was adopted?" or "How was project modeling conducted?"). Responses were received primarily from civil engineers, as well as architects, industrial engineers, company directors, and building technicians. Six non-conforming responses were discarded.

Data collection from companies

In addition to the questionnaire, data were collected from construction company websites on ongoing or completed projects, either from publicly available information or through direct access (Figure 2). The data from the 394 projects subsequently enable the extraction of indicators and, in turn, the development of the benchmarking system.

Figure 2
Data Collection from Construction Company Websites

Quantitative benchmarking calculation

The authors performed the benchmarking calculation in accordance with the HCAS. Table 2 presents the indicators, their respective weights, equations, and justifications.

Table 2
Calculation of HCAS indicators

Category	Code (I.N)	Indicator name	Reasoning	Weight* (1 – 5)	Calculation/Attribution
Design (Buildability)	I.01	Project modeling	Considers the difference CAD - BIM	3.99	Attribution: BIM = 1.00, CAD = 0.83, mixed designs in CAD and BIM authoring tools = 0.83.
	I.02	Cloud use	Considers compatibility in different design disciplines and integration in design teams	3.75	Attribution: Yes = 1.00, No = 0.00.
	I.03	Perimeter/GFA ratio	Considers the façade’s influence (lower constructability than internal walls)	3.52	4*square root of perimeter/area
	I.04	Standardization ratio of GFA	Considers the building’s standardization (standard pavements number)	4.06	number of typical floor plans / total number of pavements
	I.05	Presence of a design coordinator	Considers the reduction of clashes in design and construction	4.68	Attribution: Presence = 1.00, Absence = 0.00.
	I.06	Absence of curve elements in façades/frames	Considers the presence/absence of elements with low constructability	3.40	Attribution: Absence = 1.00, Presence = 0.00.
	I.07	Complete specification	Considers the influence of design specifications on quality, productivity and constructability	4.48	Attribution: Complete = 1.00, Incomplete or insufficient = 0.00.
	I.08	Quality of blueprints	Blueprints are still common in building works (despite digital documents): misunderstandings reduce constructability	3.30	Sum of four criteria: content clarity (QC1), text readability (QC2), scale adequacy (QC3) and paper size (QC4). All have maximum attribution of 0.25
	I.09	Wall workforce productivity	Considers using the workforce depending on the wall system	4.72	Attribution based on an improved table of constructability from BDAS system
	I.10	Transportation equipment	Considers the difference between productivity among transportation equipment	4.65	Attribution: Crane = 1.00, Elevator = 0.41, Winch = 0.16, Rope and pulley = 0.00.
	I.11	Structure workforce productivity	Considers using the workforce depending on the structural system	4.61	Attribution based on an improved table of constructability from BDAS system
	I.12	Frames’ standardization	Considers the standardization of windows/doors	4.53	Sum of SD and SW. SD Attribution: when more than 90 % of doors have up to three different types = 0.50, when the three main door types represent between 75 % and 90 % of total = 0.38, when the three main door types represent less than 75 % of total = 0.00. SW Attribution has the same criteria.
	I.13	Columns’ standardization	Considers the standardization in columns	4.36 (if no pillars, weight = 0)	Same criteria of I.13 in terms of standardization range.
	I.14	Slab thickness’ standardization	Considers the standardization of the slab thickness	4.05	area of predominant thickness in the standard floor / the whole area of the standard floor
	I.15	Constructability databases	Considers the presence of constructability databases in the construction company	2.94	Attribution: Presence = 1.00, Absence = 0.00.
Production planning	I.16	Place influence	Considers the building location in relation to building materials and specification	3.56	Attribution: construction site does not preclude any material or technology use = 1.00, Precludes = 0.00.
	I.17	Building site layout planning	Considers the existence of layout planning in the construction space	4.01	Attribution: Exists = 1.00, Does not exist = 0.00.
	I.18	Production planning	Considers the existence of production planning (e.g., formwork, positioning of tiles)	3.93	Attribution: The production planning (e.g., formwork, position of tiles, and others) exists = 1.00, Does not exist = 0.00.
	I.19	Space at the construction site for unloading	Considers that space constraints reduce constructability	3.98	Attribution: 18-wheeler truck space: 1.00, concrete-mixer truck space: 0.77, trucks need to load and unload outside the construction site: 0.00.
	I.20	Deposit space	Considers the presence of adequate storage space	3.90	Attribution: Adequate space, dedicated only for the construction site itself = 1.00, Insufficient space or shared with other construction sites = 0.00.
Execution	I.21	Organized construction site, health and safety-prone	Considers the influence of construction site organization, health, and safety on constructability	4.40	Attribution: Safe and organized site = 1.00, Unsafe/unorganized = 0.00.
	I.22	Construction team meetings	Considers the importance of meetings to reduce constructability problems in parallel with the execution phase	4.22	The number of monthly meetings with the construction team (R) is compared with the number of months in construction (M). When R ≥ M, attribution = 1.00. When R < M, I.22 = R/M
	I.23	Construction method standardization	considers the importance of standardization in operational construction methods	2.50	Attribution: Exists = 1.00, Does not exist = 0.00.

• Source: Abreu (2020).
• Note:
• ^*
  the procedure for defining the weights was explained in the Literature Review.

The benchmark calculation process is described in the following paragraphs.

Step 1

For the data derived from the questionnaire, all items directly assigned or related to dummy variables (for example, the project modeling approach (I.01)) were directly attributed values of zero, one, or intermediate scales (see column “Calculation/Attribution”, Table 2). Regarding the constructability indicators of the structural system (I.11), a typical low-income housing project with four floors plus a roof was considered for the indicator calculations, as data collected from construction companies identified this as the most frequent typology. The structural system and roof system information obtained from the questionnaire were therefore converted into indicator I.11.

Company websites provided varying levels of unstructured data availability, which influenced the acquisition of indicators for each element. Some websites included floor plans, enabling the calculation of areas, perimeters, and proportions using computer-aided design (CAD) tools. Others displayed construction site photographs, some of which were also available in public repositories such as Google Maps and Google Earth Pro, allowing for the identification of transportation methods, construction processes, roof systems, and other project characteristics that were subsequently converted into indicators. A similar approach was applied to project files provided by designers, from which geometric and standardization indicators were calculated (I.03, I.04, I.06, I.12, I.13 and I.14), also according to the column “Calculation/Attribution”, Table 2.

Step 2

The final database² was generated, by calculating as the averages of all indicators across the projects in the sample, excluding empty or unavailable data. Every indicator consists of a measurement scale and a comparison reference, which can be the baseline, the best competitor, an international standard, or even the sector average (Caribé, 2009). The average was considered an unbiased and representative estimator of a set, and the indicators have normal distributions and no outliers. In addition to the statistical aspect, the choice of the industry average follows examples from benchmarking studies such as those presented by Albertin, Kohl, and Elias (2015) and Oliveira et al. (2017), which allow for the establishment of feasible goals, since for some indicators, such as geometric and standardization indicators (I.03, I.04, I.06, I.12, I.13, and I.14), not all projects or companies could achieve the maximum indicators. In fact, quantitative benchmarking studies usually involve the definition of challenging and achievable goals (Zapelini, 2002).

This exclusion of only missing data follows a principle of Item Response Theory (IRT) for variables such as constructability, where only known or answered items are considered, while missing ones are disregarded (Soares, 2005). Carvalho (2017) points out several options that could be considered for handling missing data, as simple exclusion may lead to information loss - for example, using techniques such as casewise deletion, pairwise deletion, or mean substitution. Missing data could also be replaced by zeros (Carvalho, 2017).

Despite the availability of these techniques, the decision in this study was to adopt the available data without filling in gaps, aiming to:

1. achieve benchmarks that closely reflect the data collected in the research, avoiding distortions such as setting goals that are higher than the average values (overly rigid goals).

2. avoid filling in missing data with zeros, which could lead to lower benchmarks, resulting in goals that are easy to surpass and that do not effectively promote improvements.

Step 3

As shown in Figure 3, the HCAS (used in this benchmarking system) calculates the Partial Indicator for Design (PID), Production Planning (PIPP), and Execution (PIE), applying weights to each indicator according to the chronological order of these phases.

Figure 3
HCAS sequence of calculation

Accordingly, based on the benchmarks of indicators I.01 to I.23, benchmarks were calculated for PID, PIPP, PIE and the overall constructability indicator (IC). For construction processes involving structural columns, an additional weight and indicator are considered (I.13, see Table 2), resulting in separate definitions of PID and IC in these cases. The calculation of each partial indicator follows Equations 1 to 3, while the IC is assessed according to Equation 4.

$PID=\frac{\sum _{i=1}^{15}(weight\cdot score)}{\sum _{i=1}^{15}weight}$ Eq. 1

$PIPP=\frac{\sum _{i=16}^{20}(weight\cdot score)}{\sum _{i=16}^{20}weight}$ Eq. 2

$PIE=\frac{\sum _{i=21}^{23}(weight\cdot score)}{\sum _{i=21}^{23}weight}$ Eq. 3

$IC=\frac{\sum _{i=1}^{23}(weight\cdot score)}{\sum _{i=1}^{23}weight}$ Eq. 4

Where the weights are taken from Table 2 and I.N represents the indicator value obtained in Step 2. Calculation process is summarized in Figure 4.

Figure 4
Calculation Process

Artifact assessment

In order to evaluate the artifact, several procedures were carried out (Table 3). These procedures made it possible to observe characteristics of the artifact in use, as well as to assess the reliability of the metrics and their validity.

Table 3
Artifact assessment

Procedures	Theoretical Description	Assessment (this research)
Instantiation	Lacerda et al. (2013) point out that this evaluation can be conducted through instantiations, which allows for the analysis of the qualities of this artifact.	Presented in the Results chapter.
Reliability Analysis	Reliability refers to characteristics associated with obtaining consistent results, regardless of the researcher (Basso Júnior et al., 2016). Another definition of reliability concerns the extent to which a measurement instrument can reflect the true result, meaning that it is free from variance caused by random errors.	Dichotomous indicators or those based based solely on reading information such as the construction process are invariable (e.g., I.06).
		For indicators based on CAD tools, the test-retest technique was applied (Vituri, 2013) to verify reliability, resulting in an R² of 0.94 between readings.
		For questionnaires, a technique that demonstrates reliability and internal consistency regarding a concept or construct is the Cronbach’s alpha test (Selitto; Ribeiro, 2004). The calculated Cronbach’s alpha for the questionnaire data was 0.69, which falls within the optimal range for reliability. A value below 0.60 indicates low reliability, while a value above 0.90 may suggest redundancy or duplication among the indicators (Ribeiro et al., 2021).
Validity Analysis	Martins (2006) states that validity represents the extent to which an instrument is capable of accurately measuring a given magnitude. In this study, validity was qualitatively evaluated.	Apparent validity: used indicators and weights used have already been validated in Abreu (2020).
		Content validity: the indicators used are based on the constructability principles listed by Kifokeris and Xenidis (2017), ensuring that no important aspect was excluded within the overall context of the constructability assessment (Martins, 2006).

The planned instantiation involved applying the HCAS to 12 HIS projects (Figure 5 and Figure 6), some of which were completed, while others were still in the design phase, followed by an analysis of the indicators obtained by comparing them with the quantitative benchmarking system. Illustrating applications in social housing projects by government or development agencies, projects from Cohab (2016) and Bauru (2024) were observed.

Figure 5
Projects used for instantiation

Figure 6
Projects used for instantiation

For projects 1 to 3, the project characteristics were gathered through a review of design files and reports, complemented by contact with the construction companies and/or professionals who supervised project execution to complement any missing information. For the remaining projects, only the design files were reviewed.

DSR Conclusion step

Finally, after their definition, the benchmarks were analyzed to identify which indicators point out strengths or improvement opportunities related to constructability in the HIS projects. An evaluation was then carried out, by comparing the characteristics of the sample projects with the obtained indicators, which could serve as potential constructability goals in the context of external or competitive business benchmarking. The results were consolidated in into comparative tables, and both descriptive and inferential statistical analyses were performed.

Results

For an in-depth discussion on competitive benchmarks and constructability performance, a summary of the calculations is presented in Table 4. These benchmarks resulted in the following constructability indicators by phase and for the overall project.

Table 4
Benchmarks of partial and overall indicators of constructability

Phase	Partial indicator	Structural typology	Benchmark (0 – 1)	Source (Eq)
Design	PID	Columned construction processes (e.g., cast-in-situ reinforced concrete structures)	0.636	1
		Column-free construction processes (e.g., structural masonry or concrete walls)	0.685	1
Production planning	PIPP	All structural typologies	0.823	2
Execution	PIE		0.914	3
Project	IC	Columned construction processes (e.g., cast-in-situ reinforced concrete structures)	0.710	4
		Column-free construction processes (e.g., structural masonry or concrete walls)	0.745	4

The overall Indicator of Constructability (IC), on a scale from 0 to 1, is approximately 0.7, (indicating that there are opportunities for improvement in HIS projects within this range). These opportunities can be further explored through the individual analysis of the benchmarks presented in Table 5.

Table 5
Benchmarks of I.01 – I.23 indicators

Category	Code	Indicator name	n*	Minimum	Maximum	Benchmark
Design	I.01	Project modeling	69	0.830	1.000	0.832
	I.02	Cloud use	67	0.000	1.000	0.418
	I.03	Perimeter/GFA ratio	278	0.314	1.000	0.648
	I.04	Standardization ratio of GFA	225	0.500	1.000	0.863
	I.05	Presence of a design coordinator	67	0.000	1.000	0.791
	I.06	Absence of curve elements in façades/frames	386	0.000	1.000	0.984
	I.07	Complete specification	68	0.000	1.000	0.691
	I.08	Quality of blueprints	5	0.250	0.783	0.392
	I.09	Wall workforce productivity	191	0.050	1.000	0.435
	I.10	Transportation equipment	99	0.000	1.000	0.486
	I.11	Structure workforce productivity	191	0.214	0.920	0.633
	I.12	Frames’ standardization	68	0.380	1.000	0.804
	I.13	Columns’ standardization	0	0.000	0.000	0.000
	I.14	Slab thickness’ standardization	5	1.000	1.000	1.000
	I.15	Constructability databases	67	0.000	1.000	0.612
Production planning	I.16	Place influence	67	0.000	1.000	0.716
	I.17	Building site layout planning	62	0.000	1.000	0.887
	I.18	Production planning	64	0.000	1.000	0.750
	I.19	Space at the construction site for unloading	316	0.000	1.000	0.977
	I.20	Deposit space	66	0.000	1.000	0.773
Execution	I.21	Organized construction site, health and safety-prone	135	0.000	1.000	0.837
	I.22	Meetings with a construction team	62	0.000	1.000	0.952
	I.23	Standardization in the construction method	62	0.000	1.000	0.984

• Note:
• ^*
  n represents the number of projects considered for each indicator. Some projects did not contain sufficient information to calculate the respective indicator benchmarks. Consequently, n may differ from the total number of projects in the sample (394).

In the following sections, the results of the Instantiation with the artifact will be presented, followed by analyses related to the quantitative benchmarking system.

Instantiation in HIS projects

Table 6 presents the analysis of the Instantiation indicators in relation to the quantitative benchmarking system. Among projects 01 to 03, Project 02 exhibits the highest constructability, whereas Project 01 shows the lowest IC (below the Brazilian benchmark) and the greatest number of indicators falling below the benchmarks. This outcome can be attributed to several factors, such as the absence of a collaborative environment for product design development (indicator I.02) or the adoption of traditional masonry for walls, which are labor-intensive and evaluated with low indicators due to their negative impact on constructability (indicator I.09).

Table 6
Instantiation results

Project	Construction process (structure + walls)	Indicators
		I.01	I.02	I.03	I.04	I.05	I.06	I.07	I.08	I.09	I.10	I.11	I.12	I.13	I.14	I.15		I.16	I.17	I.18	I.19	I.20	I.21	I.22	I.23	PID	PIPP	PIE	IC
01	Cast in situ reinforced concrete with masonry walls	0.83	0.00	0.81	0.93	1.00	1.00	1.00	0.75	0.05	0.00	0.35	1.00	1.00	1.00	0.00		1.00	0.00	0.00	1.00	1.00	1.00	1.00	1.00	65.14	59.03	100.00	68.08
02	Structural masonry	0.83	1.00	0.81	1.00	0.00	1.00	1.00	1.00	0.71	1.00	0.46	0.89		1.00	0.00		1.00	0.00	1.00	1.00	1.00	1.00	1.00	1.00	76.49	79.31	100.00	80.11
03	Cast in situ reinforced walls	0.83	1.00	0.87	1.00	0.00	1.00	1.00	0.50	0.71	1.00	0.86	0.38		1.00	0.00		1.00	1.00	0.00	1.00	1.00	1.00	1.00	1.00	73.16	79.72	100.00	78.04
04	Cast in situ reinforced concrete with masonry walls	0.83		0.68	1.00		1.00			0.05		0.33	0.00	1.00	1.00					1.00
05	Structural masonry	1.00	1.00	0.69	1.00		1.00			0.10		0.38	0.50		1.00
06	Structural masonry	1.00	1.00	0.75	0.75	1.00	1.00			0.10		0.41	1.00		1.00						1.00
07	*			0.71	0.87								0.50		1.00						1.00
08	*	0.83		0.65	0.75				0.13				0.88
09	Cast in situ reinforced concrete with masonry walls. Prefabrication in the slabs			0.38	0.80		1.00			0.05	1.00	0.45									1.00
10	Structural masonry	0.83	0.00	0.34	1.00		1.00		0.31	0.10		0.38	0.88		1.00						1.00
11	Metal structure with ceramic masonry walls			0.65	1.00		1.00			0.05		0.74			1.00						1.00
12	*	0.83		0.67	1.00		1.00		0.06						1.00						1.00
	Benchmarks – construction process with columns	0.83	0.42	0.65	0.86	0.79	0.98	0.69	0.39	0.44	0.49	0.63	0.80	0.00	1.00	0.61		0.72	0.89	0.75	0.98	0.77	0.84	0.95	0.98	63.62	82.34	91.35	70.95
	Benchmarks – construction process without columns	0.83	0.42	0.65	0.86	0.79	0.98	0.69	0.39	0.44	0.49	0.63	0.80		1.00	0.61		0.72	0.89	0.75	0.98	0.77	0.84	0.95	0.98	68.51	82.34	91.35	74.50
	Legend:
	More or equal to benchmark
	Less than benchmark
	unavailable / not evaluated

These and other findings underscore the relevance of the artifact. In the remaining projects (04 to 12), the analyses of individual indicators also highlight further opportunities for improvement, such as, for instance:

1. Project 04: the level of standardization in the frames (I.12 - Frames’ standardisation) is lower than desirable, resulting in greater complexity in management and control, as well as in the execution of the corresponding openings by workers, which leads to reduced constructability. Since this is a "standard design", it represents an aspect that can be improved in future projects;

2. Project 09: The floor plan has an elongated shape, which increases the facade surface area and results in reduced constructability. This characteristic is reflected in indicator I.04 - Standardisation ratio of GFA; and

3. Project 11: Indicator I.09 (Wall workforce productivity) is well below the benchmark due to the use of vertical partitions in ceramic block masonry - a construction method that is labor-intensive, involves a large number of components, and consequently results in lower constructability. In contrast, the structural system in this project is above the benchmark, as it employs metal structures.

Throughout this instantiation, it was observed that the evaluated projects, in some cases, exhibit characteristics highlighted by the overall benchmark analysis, such as the presence of large units or garden units on the ground floor. These features affect the levels of standardization between floors, influence learning effects, and consequently impact constructability. These aspects will be explained in the following section.

Analysis of I.01 to I.23 benchmarks

The indicator I.03 (Perimeter/GFA ratio) reflects lower constructability in façades and considers square floor plants as the optimal situation. In Figure 7, the frequency distribution of I.03 shows the existence of projects that are fully optimized in this ratio.

Figure 7
Perimeter/GFA ratio

The most frequent category falls within the range of 0.626 to 0.688, with a benchmark of 0.648. This frequent range can serve as a reference, given that the unrestricted adoption of square-shaped floor plans is not feasible due to other commercial factors and land utilization constraints. Nevertheless, many projects remain extensive and elongated, with large façade areas relative to their built area - a characteristic that could be improved.

The need to improve this indicator is justified because façade finishings are more difficult to execute than in internal walls due to more complex execution requirements. Façades demand materials, equipment, and controls that are more demanding than those required for internal walls, such as, façade access equipment, stricter quality control for external finishings, and window-opening waterproofing. These conditions which reduce constructability on façades, result in lower productivity than in internal walls.

Kifokeris and Xenidis (2017) highlighted that standardization is a constructability principle due to its contribution to productivity gains. Previous research studies have addressed this aspect only qualitatively. The benchmarks presented in Table 5 (e.g., I.04, I.12 to I.14) make it possible to analyze this aspect quantitatively and, when feasible, to optimize it.

For indicator I.04 (reflecting the prevalence of typical floors relative to the total number of floors), a benchmark of 0.863 was observed. These data are strongly influenced by the most frequent characteristics in the sample, which includes buildings of four to five floors, with at least one floor differing from the typical floor. The main differences between non-repetitive and repetitive floors are the inclusion of garden units and dwellings for people with disabilities (PWD). Consequently, standardization levels are below the maximum indicator value (1.000); however, legal and commercial aspects are still satisfied.

Regarding indicator I.11, the benchmark was 0.633, reflecting the predominant structural systems in Brazil, which is more labor-intensive and result in lower productivity compared to other structural systems, thereby reducing constructability. In the sample of 394 projects, 43 % employed structural masonry, 46 % used cast-in-situ reinforced concrete walls, 6 % adopted cast-in-situ reinforced concrete structures and 4 % used more than one structural system or other types.

The indicator I.01 is concerned with design modelling, taking into account that the use of Building Information Modeling (BIM) is likely to lead to few construction problems on site by reducing interferences, enabling clash detection, generating detailed production designs and improving safety conditions (Lin et al., 2017). It presents a benchmark of 0.832, very close to the 0.830 constructability indicator for CAD designs. This demonstrates that there are still many improvement opportunities related to the adoption of BIM and leveraging its benefits for constructability. This indicator suggests that the HIS market has not yet fully adopted BIM in its processes, although its use is increasingly widespread among engineering and architecture professionals in Brazil.

The indicator I.10 is related to the use of transportation equipment, which is essential for improving productivity and constructability. It has a benchmark of 0.486. This value is similar to the benchmark assigned to construction elevators (cable or rack-and-pinion), and can also be explained by the 43 % of projects employing structural masonry, that requires more intensive use of such equipment. Cranes are often underutilized due to their cost and because many projects have a lack of material-handling demands or scale needed to justify this investment.

Regarding construction site characteristics, (specifically production planning) the availability of space on construction sites has not been a significant hindrance to constructability in terms of restricted movement. Site with insufficient space to accommodate a concrete mixer truck are evaluated at 0.00, whereas sites with space exceeding that required for an 18-wheeler truck are evaluated at 1.00. Indicator I.19 shows a benchmark of 0.977, approaching the maximum value of 1.000, which corresponds to construction sites with space available for at least one delivery vehicle within the site’s boundaries.

Another important aspect of construction sites concerns the availability and allocation of storage areas. The Brazilian benchmark for this factor is 0.773, indicating room for improvement through the adjustment of stock levels to workflow demands and the allocation of storage space exclusively for the construction site rather than shared with neighboring sites. This principle aligns with constructability concepts discussed by Kifokeris and Xenidis (2017) and with approaches aimed at improving construction projects, particularly lean construction (Koskela, 1992; Araújo et al., 2023).

Discussion

In the instantiation, based on the PIPP indicator, an opportunity for improvement was identified for Construction Companies "A" and "B," which oversee projects 01 to 03. Without the quantitative benchmarking system, this improvement opportunities would not have been evident: if only the indicators of projects 01 to 03 were considered, the HCAS indicators would have allowed only a limited comparison among these projects, without highlighting that projects 02 and 03 (although superior to Project 01 in terms of PIPP) still require improvements.

The partial indicators for Projects 01 to 03 (PID and PIE) are at or above the benchmarks, however, there are drawbacks in production planning, represented by the PIPP indicator, which is below than the benchmark. Although there is a well-established practice of developing the construction product; it is essential to define how it will be executed to ensure that constructability is not compromised by inadequate construction techniques, service interference, or other issues.

Thus, the instantiation demonstrates that the quantitative benchmarking system enables a more comprehensive assessment of project constructability (external business benchmarking, see Table 1), without being limited to a small dataset or requiring the construction of a data series within a single company (internal business benchmaking). Analysis biases are also reduced, as external benchmarking was performed, encompassing evaluations form 83 companies. This approach supports continuous improvement in HIS projects in a more effective and appropriate manner.

When analyzing the indicators of the quantitative benchmarking system, the potential for improving constructability becomes evident - for example, by adopting alternative construction processes or improving the level of standardization in projects. This quantitative benchmarking system can be used to support companies seeking to enhance their HIS projects, as well as by the government funding agencies aiming to improve financed projects.

Finally, it should be noted that the quantitative benchmarking system represents both a theoretical and a practical advance in knowledge on constructability. As highlighted by Abreu and Marchiori (2023), quantitative benchmarking studies were previously very limited, being difficult not only to assess the general constructability indicator, but also the constructability related to specific items.

Conclusion

The quantitative benchmarking system highlights potential improvements in the constructability of projects in Brazilian Social Housing Projects (HIS). By applying the Design Science Research (DSR) method, a comprehensive quantitative benchmarking system was established, based on data from 394 projects, enabling a robust and reliable evaluation of constructability.

Relying solely on an indicator system and its scoring scale (without considering broader market conditions) would not allow for a comprehensive understanding of the sector or the establishment of goals based on appropriate benchmarks. As demonstrated in the instantiation, merely comparing a few projects, whether from a limited number of companies or from a single company, may fail to clearly reveal opportunities for improvement or determine optimal levels of critical variables, such as standardization. The proposed benchmarking system overcomes these limitations by consolidating data from multiple companies and providing benchmarks that reflect current practices and areas for improvement.

Based on the collected data, several improvement opportunities were identified, including the broader adoption of BIM, enhanced production planning, and more tailored stock management. In the private sector, HIS projects with greater constructability can be prioritized as investment opportunities. In the public sector, funding agencies can stimulate improvements in the industry by offering more attractive financing conditions for projects that demonstrate higher constructability. The quantitative benchmarking system presented here can serve as a guideline for such incentives, promoting continuous improvement in HIS projects.

This study also represents a theoretical and practical advance in constructability knowledge by enabling the identification not only of overall constructability levels but also of deficiencies in specific indicators. Future research could focus on developing and integrating BIM tools with the benchmarks and scaling identified in this study, allowing the use of these data to support project processes and decision-making aimed at enhancing constructability. Furthermore, future studies could deepen the understanding of constructability in HIS within the construction sector and expand the HCAS system. In line with this research, other types of projects could be considered, with benchmark assessments conducted for each project type.

Acknowledgments

This study was partially funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brazil (Capes) – Finance Code 001.

• ABREU, J. P. M. de; MARCHIORI, F. F.; SIGNOR, R.; Constructability assessment of social housing projects in Brazil: a quantitative benchmarking system. Ambiente Construído, Porto Alegre, v. 25, e146203, jan./dez. 2025. ISSN 1678-8621 Associação Nacional de Tecnologia do Ambiente Construído. http://dx.doi.org/10.1590/s1678-86212025000100935
• 1
  The complete questionnaire is available at: https://www.engjpma.com.br/2016/08/Questionnaire.PaperRAC.ConstructabilityBenchmarking.html.
• 2
  https://www.engjpma.com.br/2016/08/ConstructabilityIndicatorsDataset.BenchmarkingPaperRAC.html.

Declaração de Disponibilidade de Dados

Os dados de pesquisa estão disponíveis em repositório indicado no corpo do artigo.

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Edited by

• Editores:
  Carlos Torres Formoso e Giane de Campos Grigoletti

Publication Dates

• Publication in this collection
  01 Dec 2025
• Date of issue
  2025

History

• Received
  07 Mar 2025
• Accepted
  06 Oct 2025